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deep-research

Produce evidence-based research briefs before software builds.

Updated Aug 23, 2026
Search Tags:#architecture#research#library discovery#competitor analysis#product planning#platform capabilities#ecosystem signals
One-click install
npx skills add https://github.com/redbananastudios/ai-library --skill deep-research-redbananastudios
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: deep-research
Source: https://github.com/redbananastudios/ai-library/tree/main/generated/claude/skills/deep-research
Command: npx skills add https://github.com/redbananastudios/ai-library --skill deep-research-redbananastudios

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you stop guessing and start building with confidence by producing a thorough research brief before coding or designing a new project.

Core Features & Use Cases

  • Pre-build discovery across depth levels: turn vague intent into focused, wide, or deep research plans (focused for single decisions, wide for landscape/spec work, deep for major builds).
  • Local + web + ecosystem investigation: scans existing local projects for reusable patterns while also gathering competitor insights, plugin ecosystem signals, and community demand.
  • Actionable synthesis into a structured brief: outputs a decision-ready research artifact covering users, competitors, technical options, platform capabilities, risks, and next phases.

Use case: You want to build a new note app—this Skill helps you research prior art, competitor gaps, required platform capabilities, and suitable component/libraries so your eventual spec and implementation are grounded in evidence.

Quick Start

Ask for deep research on your build idea by specifying what you want to build, who it’s for, constraints, and any existing repos or local projects to inspect.

Frequently Asked Questions about deep-research

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I research competitor gaps and platform capabilities before building a new app?▼

Pre-build research involves gathering intent and scanning local projects alongside broad web analysis of competitors, plugins, issues, and reviews to produce an evidence-based research brief. It covers user needs, competitor gaps, technical options, and platform capabilities.

What is the best way to select libraries and components for a software architecture project?▼

Library discovery and component selection rely on ecosystem signal analysis across competitors, plugins, issues, and reviews. Evaluating these signals ensures your chosen architecture components are grounded in current community demand and verified platform capabilities.

How do I structure product planning for focused, wide, or deep research scopes?▼

Product planning research scopes are structured by intent: focused for single decisions, wide for landscape and spec work, and deep for major builds. Each scope requires documented output synthesis to transform raw ecosystem signals into a decision-ready artifact.

Does this pre-build research approach work with existing local projects?▼

Yes, the research process scans existing local projects to identify reusable patterns and architecture decisions. This local investigation is combined with web and ecosystem analysis to ensure new builds leverage prior work effectively.

When should I not rely on outdated training assumptions for platform capability verification?▼

You should avoid outdated training assumptions during platform capability verification whenever current changelog or documentation discovery reveals new constraints. Relying on stale data risks missing updated platform capabilities or deprecated features critical to your architecture.

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